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name: mixtures-and-classifiers description: "Guides pomegranate GeneralMixtureModel and BayesClassifier workflows, including heterogeneous mixture fitting, supervised probabilistic classification, posterior probabilities, priors, EM settings, and component distributions." disable-model-invocation: true metadata: disco-role: operating license: MIT

mixtures-and-classifiers

Use this sub-skill when

Use this sub-skill for unsupervised mixture modeling with GeneralMixtureModel and supervised probabilistic classification with BayesClassifier. These models are useful when a task asks for Gaussian mixtures, heterogeneous mixtures, EM fitting, class-conditional distributions, posterior component/class probabilities, prior-weighted inference, or semi-supervised-style hard priors.

Start here

Typical imports:

from pomegranate.distributions import Normal, Exponential
from pomegranate.gmm import GeneralMixtureModel
from pomegranate.bayes_classifier import BayesClassifier

Read references/api-reference.md for constructor signatures, fitting recipes, and priors. Run scripts/smoke_mixtures_classifiers.py after installation for a tiny check that both model families fit and predict.

Core workflow

1. Build component distributions. Create one pomegranate distribution or probabilistic model per component/class. Use ../distributions/SKILL.md if component selection is the hard part.
2. Choose unsupervised vs supervised. Use GeneralMixtureModel(distributions, ...) when labels are unknown; use BayesClassifier(distributions, ...) when class labels y are provided.
3. Fit on 2D data. Mixtures and classifiers expect feature matrices shaped (n, d).
4. Inspect posterior assignments. Use predict, predict_proba, or predict_log_proba for component/class assignment probabilities.
5. Use priors deliberately. Priors must be probabilities with valid shape and row sums. Read ../../references/feature-guide.md before using hard labels or soft prior weights.
6. Tune EM only when necessary. max_iter, tol, init, random_state, and verbose affect mixture fitting and initialization.

Route elsewhere when

• The task is only about selecting or fitting a base distribution: read ../distributions/SKILL.md.
• The model is a Bayesian network or factor graph: read ../graph-models/SKILL.md.
• The model is a Markov chain or HMM over sequences: read ../sequence-models/SKILL.md.
• The task is KMeans clustering or centroid initialization directly: read ../clustering/SKILL.md.

Guardrails

• BayesClassifier.fit requires y; GeneralMixtureModel.fit does not.
• The order of distributions defines component/class indices, so record the mapping before interpreting predictions.
• If uninitialized component distributions are supplied to GeneralMixtureModel, pomegranate initializes them with KMeans before EM.
• Validate a small example before disabling check_data or moving to mixed precision/compiled execution.
• Read references/troubleshooting.md when priors, labels, component initialization, or EM convergence fails.